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| Rotor space-time domain image enhancement and vibration measurement method |
| WANG Qingjian1, WANG Sen1, WU Xing2, LIU Xiaoqin1, LIU Tao1 |
1. College of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China;
2. Yunnan Electromechanical Vocational Technical College, Kunming 650201, China |
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Abstract The main factors for improving the accuracy of visual vibration measurement depend on the number of samples in the captured image per unit period and the sharpness of the edge of the vibrating target in the image. However, the limited acquisition frequency and inherent resolution of industrial cameras will lead to obvious offset errors in the final obtained timing displacement signals due to the loss of spatial-temporal information. Therefore, this paper proposes a method for spatial-temporal enhancement and vibration measurement of rotor images. A fitting sample is inserted between two adjacent sample images through the video frame interpolation algorithm, thereby enhancing the number of samples collected in a unit period. In order to alleviate the phenomenon of blur, distortion and noise in the collected low-resolution images, the super-resolution reconstruction algorithm is used to restore the high-frequency information in the images. After weighing the time consumption of the algorithm and the consumption of computing resources, this paper integrates the video frame insertion and video reconstruction tasks to realize the feature information sharing of the two model tasks. The experimental results tested on the self-made high-speed vibration rotor image dataset show that compared with the current advanced algorithm, the model constructed in this paper is improved by 0.3dB and the lightweight model has better reconstruction accuracy than the advanced model while reducing the model parameters by 17.9%. The time-domain and frequency-domain signals measured at two sampling frequencies show that the vibration signals obtained by the proposed algorithm have better periodicity and stability.
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Received: 15 March 2022
Published: 15 April 2023
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